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Abstract
This study uses variables from the Organisation for Economic Co-operation and Development (OECD) Centre's Gender, Institutions and Development (GID) Database to construct the Social Institutions and Gender Index (SIGI) and its subindices Family code, Civil liberties, Physical integrity, Son preference, and Ownership rights. Instead of measuring gender inequality in outcomes, the SIGI and its subindices measure long-lasting social institutions defined as societal practices and legal norms that frame gender roles. The SIGI combines them into a multidimensional index of women's deprivation caused by gendered social institutions. Inspired by the Foster–Greer–Thorbecke poverty measures, the SIGI offers a new way of aggregating gender inequality by penalizing high inequality in each dimension and allowing only partial compensation between subindices. The indices identify countries and dimensions of gendered social institutions that deserve attention. Empirical results confirm that the SIGI complements other gender-related indices.
NOTES ON CONTRIBUTORS
Boris Branisa works as senior researcher at INESAD, an independent organization in Bolivia promoting scientific research on themes relevant to developing countries. He has held several positions at the Central Bank of Bolivia, and worked as Postdoctoral Researcher at the University of Mannheim. His main research interests are development economics, applied econometrics, and impact evaluation. Boris holds a PhD in Economics from the University of Cöttingen.
Denis Drechsler is Policy Analyst at the Food and Agriculture Organization of the United Nations (FAO). He is the author of several articles and papers in the areas of migration, gender equality, and informal employment. Prior to joining FAO, Denis worked for the OECD Development Centre and the Poverty Reduction and Economic Management Group of the World Bank. Other employments include consultancies for the German Parliament and the European Institute for International Economic Relations. Denis completed his graduate studies in political science and economics at Potsdam University, the Institute d'Etudes Politiques de Grenoble, and the University of Wisconsin.
Johannes Jütting is Manager of the PARIS21 (Partnership in Statistics for Development in the 21st Century) Secretariat, a global partnership supporting strengthening statistical capacity development in the Global South, hosted at the OECD in Paris. Before joining PARIS21, Johannes was Head of the Poverty Reduction Team at the OECD Development Centre. His responsibilities include conceptualizing, managing, and providing leadership in the areas of employment, gender, social protection, and migration. He has published extensively in peer-reviewed journals such as World Development and the Journal of Human Development. He holds a PhD in agricultural economics from Humboldt University (Berlin) and received his habilitation in development economics from the University of Bonn.
Stephan Klasen is Professor of Development Economics and Empirical Economic Research at the University of Göttingen, where he is also head of the Ibero-American Institute and coordinator of the Courant Research Centre “Poverty, Equity and Growth in Developing and Transition Countries: Statistical Methods and Empirical Analysis.”
Maria Ziegler is Researcher at the German Development Institute / Deutsches lnstitut für Entwicklungspolitik. She holds a PhD in development economics from the University of Cöttingen. She worked as a consultant for OECD, GTZ, and the Bertelsmann Foundation on issues of gender, poverty reduction policies, and institutional reform.
Acknowledgments
We thank Oleg Nenadic, Carola Grün, and Axel Dreher from the University of Göttingen, as well as members of the International Working Group on Gender, Macroeconomics and International Economics (GEM-IWG), participants at the 2009 Far East and South Asia Meeting of the Econometric Society and at the 2009 Singapore Economic Review Conference, for valuable comments and discussion.
Notes
1 For a detailed review of these and other measures, see A. Geske Dijkstra (Citation2006), StephanKlasen and Dana Schüler (2011), and Irene van Staveren (Citation2011).
2 Please note that this study discusses variables and aggregation procedure of the 2009 formulation of the SIGI. In 2012, the OECD Development Centre presented a new version of the SIGI, which uses a very similar coding and aggregation procedure but slightly different variables that also tend to refer to a later period. For more information, see OECD (Citation2012).
3 For further analyses that use the SIGI or its subindices as explanatory or as dependent variables, see Boris Branisa and Maria Ziegler (2010); Nicola Jones, Caroline Harper,and Carol Watson (Citation2010); Seo-Young Cho (Citation2010); Niklas Potrafke and HeinrichUrsprung (Citation2011); Johannes P. Jütting, Angela Luci, and Christian Morrisson (Citation2012); and Boris Branisa, Stephan Klasen, and Maria Ziegler (2013).
4 See Stephan Klasen (Citation2007) for a discussion.
5 The data are available at the web pages supported by the OECD Development Centre; see OECD (Citationn.d.a, Citationb).
6 Two of the variables (Early marriage and Female genital mutilation) are continuous. The other indicators measure social institutions on an ordinal categorical scale.
7 Usual PCA is only valid for normally distributed variables (Ian T. Jolliffe Citation1986). This assumption is violated in this case, as the data include variables that are ordinal, and hence the Pearson correlation coefficient used for PCA is not appropriate. Following Kolenikov and Angeles (Citation2009), we use polychoric PCA, which relies on polychoric and polyserial correlations. These correlations are estimated with maximum likelihood, assuming that there are latent normally distributed variables that underlie the ordinal categorical data.
8 The first principal component is the weighted sum of the standardized original variables that captures as much of the variance in the data as possible. The proportion of explained variance by the first principal component is 70 percent for Family code, 93 percent for Civil liberties, 60 percent for Physical integrity, and 87 percent for Ownership rights. The standardization of the original variables is done as follows: In the case of continuous variables, one subtracts the mean and then divides by the standard deviation; in the case of ordinal categorical variables, the standardization uses results of an ordered probit model.
9 Acceptance of polygamy in the population might proxy actual practices better than the formal indicator legality of polygamy, as laws might be changed faster than practices. Therefore, the acceptance variable is the first choice for the subindex Family code. The reason for using legality when acceptance is missing is to increase the number of countries included.
10 Originally, Missing women was part of the dimension Physical integrity, but we argue that missing women reflects another dimension of gender inequality. The two components of Physical integrity, Violence against women and Female genital mutilation, focus on freedom from bodily harm, while Missing women is a more general proxy for Son preference that results in skewed fertility strategies and allocation decisions favoring sons. It also turns out that the statistical association between the two indicators of Physical integrity and Son preference is rather weak, suggesting that it is measuring a different concept.
11 Note that these indicators are based on legal rights, not actual prevalence. See CherylR. Doss, Caren Grown, and Carmen Diana Deere (2008) for a careful discussion of how to generate micro-based indicators of asset ownership by gender.
12 Some differences between the SIGI and the FGT measures must be highlighted. In the case of the SIGI, we are aggregating across dimensions and not over individuals. Moreover, in contrast to the income case, a lower value of xi is preferred, and the normalization achieved when dividing by the poverty line z is not necessary as , i=1, … , n.
13 The subindices are computed only for countries that have no missing values on the relevant input variables. In the case of the SIGI, only countries that have values for every subindex are considered.
14 Most of the results we report here can be deduced from the tables with the country rankings. We did not report separate tables for this analysis, but they are available on request.
15 The only exception here is in MENA, where Inheritance rights uniformly score 0.5.
16 Moreover, from a statistical point of view, the rank correlation coefficient Kendall Tau-b between the other variable in the subindex – namely Freedom of movement, and Civil liberties as it is defined here – is close to 0.9. This suggests that excluding the variable Freedom of dress, and having Freedom of movement as the only variable capturing the freedom of women's social participation, would not lead to a major change in the ranking of countries according to this subindex.
17 The GGI is a geometric mean of the ratios of female to male achievements in the dimensions health, education, and labor force participation. “Capped” means that every component is capped at one before calculating the geometric mean. This is done to ensure that only gaps hurting women are considered. GGI can be more directly interpreted as a measure of gender inequality, while the GDI measures human development penalizing gender inequality. The GEM has three components: political representation, representation in senior positions in the economy, and power over economic resources. The most problematic component is power over economic resources proxied by earned incomes. This component measures female and male earned incomes using income levels adjusted by gender gaps; it is empirically largely driven by income levels, not by gender gaps. To avoid this problem, the revised GEM only uses income shares of men and women in this component.
18 We have also computed the Pearson correlation coefficient between SIGI and all the measures. The Pearson correlation coefficient is lower than 0.80 for all correlations.
19 It must be noted that the samples used for computing the rank correlation differ from case to case, ranging from thirty-three countries (GEM) to ninety-nine (WOSOC).
20 See Branisa, Klasen, and Ziegler (Citation2013) and Branisa and Ziegler (Citation2010) for more detailed assessments of the empirical relevance of the SIGI and its subindices in explaining development outcomes.
21 As the number of observations is lower than 100, we use HC3 robust standard errors proposed by Russell Davidson and James G. MacKinnon (Citation1993) to account for possible heteroscedasticity in our data.
22 Using the difference between the HDI and the GDI, another possible measure of gender inequality, the impact of the SIGI is similarly significant.
23 Results are available upon request. The type of robust regression we perform uses iteratively reweighted least squares and is described in Lawrence C. Hamilton (Citation1992). A regression is run with ordinary least-squares, then case weights based on absolute residuals are calculated, and a new regression is performed using these weights. The iterations continue as long as the maximum change in weights remains above a specified value.
24 See Doss, Grown, and Deere (Citation2008) for suggestions regarding developing micro data on gender inequality in asset holdings.